These are not the k-mers you are looking for: efficient online k-mer counting using a probabilistic data structure

TL;DR

The paper introduces khmer, a memory-efficient online k-mer counting software using a Count-Min Sketch, enabling fast, scalable analysis of sequencing data with controlled overcounting, outperforming existing tools in speed and memory usage.

Contribution

It presents a novel k-mer counting approach based on a probabilistic data structure, improving memory efficiency and enabling online analysis compared to traditional exact methods.

Findings

khmer is faster and more memory-efficient than existing tools

it introduces systematic overcounting due to the Count-Min Sketch

khmer effectively supports error profiling and read normalization

Abstract

K-mer abundance analysis is widely used for many purposes in nucleotide sequence analysis, including data preprocessing for de novo assembly, repeat detection, and sequencing coverage estimation. We present the khmer software package for fast and memory efficient online counting of k-mers in sequencing data sets. Unlike previous methods based on data structures such as hash tables, suffix arrays, and trie structures, khmer relies entirely on a simple probabilistic data structure, a Count-Min Sketch. The Count-Min Sketch permits online updating and retrieval of k-mer counts in memory which is necessary to support online k-mer analysis algorithms. On sparse data sets this data structure is considerably more memory efficient than any exact data structure. In exchange, the use of a Count-Min Sketch introduces a systematic overcount for k-mers; moreover, only the counts, and not the k-mers, are stored. Here we analyze the speed, the memory usage, and the miscount rate of khmer for generating k-mer frequency distributions and retrieving k-mer counts for individual k-mers. We also compare the performance of khmer to several other k-mer counting packages, including Tallymer, Jellyfish, BFCounter, DSK, KMC, Turtle and KAnalyze. Finally, we examine the effectiveness of profiling sequencing error, k-mer abundance trimming, and digital normalization of reads in the context of high khmer false positive rates. khmer is implemented in C++ wrapped in a Python interface, offers a tested and robust API, and is freely available under the BSD license at github.com/ged-lab/khmer.